Morocco

The recent rapid real-world adoption of vision-language models (VLMs) raises concerns about how social biases encoded in language may propagate into visual generation. In this work, we examine whether socioeconomic stereotypes, expressed through occupation and income-related linguistic cues in prompts, systematically influences skin-tone representations in text-to-image (T2I) generation, with a focus on colorism as a visual marker of social inequality. We first benchmark 3 small VLMs and 60 human annotators on the Monk Skin Tone (MST) scale using the MST-E dataset. We then conduct a large-scale T2I generation study in which we systematically vary the linguistic framing of income in prompts describing 210 occupations, producing over 2,500 portraits across 3 large VLMs. The skin-tone audit of the portraits by the best-performing annotator (GPT-5 mini) reveals strong color bias: high-income prompts consistently produce lighter-skinned faces, with prompt constraints only modestly attenuating this effect. Bias magnitude varies across generators, with GPT-5 Image-mini and Gemini-2.5 Flash-Image exhibiting more pronounced shifts in MST than Grok-2 Image. Our findings indicate that VLMs encode and amplify ethnoracialized socioeconomic stereotypes in language-conditioned image generation, underscoring the need for cross-modal fairness audits and human-centered evaluations.

EACL 2026

Colorism in Multimodal AI: An Empirical Exploration of Socioeconomic Linguistic Bias in Text-to-Image Generation

bias auditing

multimodal ai

text-to-image generation

vision-language models

fairness

## Welcome to EMNLP 2024! 
We are excited to welcome you to one of the most prominent conferences in the field of Natural Language Processing. This year, EMNLP 2024 is being held in a hybrid format,
offering both virtual and in-person participation in beautiful Miami. Due to a record-breaking number of submissions, we've expanded the total number of accepted papers to accommodate more cutting-edge research from around the globe.
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EMNLP 2024

EMNLP 2024 will take place in Miami, Florida from Nov 12th to Nov 16th, 2024, at the Hyatt Regency Miami Hote and on Underline for remote participants.

technical paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)* 

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

We present a new deliberation interface that enables users to engage with multiple large language models (LLMs), coordinated by a moderator agent that assigns roles, manages turn-taking, and ensures structured interaction. Grounded in argumentation theory, the system fosters critical thinking through user–LLM dialogues, real-time summaries of agreements and open questions, and argument maps. Rather than treating LLMs as mere answer providers, our tool positions them as reasoning partners, supporting epistemically responsible human–AI collaboration. It exemplifies hybrid argumentation and aligns with recent calls for “reasonable parrots,” where LLM agents interact with users guided by argumentative principles such as relevance, responsibility, and freedom. A user study shows that participants found the tool easy to use, perspective-enhancing, and promising for research, while suggesting areas for improvement. We make the deliberation interface accessible for testing and provide a recorded demonstration.

ARGSBASE: A Multi-Agent Interface for Structured Human–AI Deliberation

This paper presents a new open-source web application for simultaneous speech-to-text translation. The system translates live Estonian speech into English, Russian, and Ukrainian text, and also supports English-to-Estonian translation. Our solution uses a cascaded architecture that combines streaming speech recognition with a recently proposed LLM-based simultaneous translation model. The LLM treats translation as a conversation, processing input in small five-word chunks. Our streaming speech recognition achieves a word error rate of 10.2\% and a BLEU score of 26.1 for Estonian-to-English, significantly outperforming existing streaming solutions. The application is designed for real-world use, featuring a latency of only 3 - 6 seconds. The application is available at https://est2eng.vercel.app.

Simultaneous Speech-to-Text Translation Web Application for Estonian

Entity linking (EL) aims to disambiguate named entities in text by mapping them to the appropriate entities in a knowledge base. However, it is difficult to use some EL methods, as they sometimes have issues in reproducibility due to limited maintenance or the lack of official resources. To address this, we introduce \entitylinkings, a unified library for using and developing entity linking systems through a unified interface. Our library flexibly integrates various candidate retrievers and re-ranking models, making it easy to compare and use any entity linking methods within a unified framework. In addition, it is designed with a strong emphasis on API usability, making it highly extensible, and it supports both command-line tools and APIs. Our code is available on GitHub and distributed via PyPI under the MIT license. The video is available on YouTube.

entity-linkings: A Unified Library for Entity Linking

Bangla is one of the world’s most widely spoken languages, yet it remains significantly under-resourced in natural language processing (NLP). Existing efforts have focused on isolated tasks such as Parts of Speech (POS) tagging and Named Entity Recognition (NER), but comprehensive, integrated systems for core NLP tasks including Shallow Parsing and Dependency Parsing are largely absent. To address this gap, we present BanSuite, a unified Bangla NLP ecosystem developed under the EBLICT project. BanSuite combines a large-scale, manually annotated Bangla Treebank with high-quality pretrained models for POS tagging, NER, shallow parsing, and dependency parsing, achieving strong in-domain baseline performance (POS: 90.16 F1, NER: 90.11 F1, SP: 86.92 F1, DP: 90.27 UAS). The system is accessible through a Python toolkit (Bkit) and a Web Application, providing both researchers and non-technical users with robust NLP functionalities, including tokenization, normalization, lemmatization, and syntactic parsing. In benchmarking against existing Bangla NLP tools and multilingual Large Language Models (LLMs), BanSuite demonstrates superior task performance while maintaining high efficiency in resource usage. By offering the first comprehensive, open, and integrated NLP platform for Bangla, BanSuite lays a scalable foundation for research, application development, and further advancement of low-resource language technologies. A demonstration video is provided to illustrate the system’s functionality in https://youtu.be/3pcfiUQfCoA

BanSuite: A Unified Toolkit and Software Platform for Low-Resource NLP in Bangla (presented virtually)

Digitization of medical records often relies on smartphone photographs of printed reports, producing images degraded by blur, shadows, and other noise. Conventional OCR systems, optimized for clean scans, perform poorly under such real-world conditions. This study evaluates compact multimodal language models as privacy-preserving alternatives for transcribing noisy clinical documents. Using obstetric ultrasound reports written in regionally inflected medical English common to Indian healthcare settings, we compare eight systems in terms of transcription accuracy, noise sensitivity, numeric accuracy, and computational efficiency. Compact multimodal models consistently outperform both classical and neural OCR pipelines. Despite higher computational costs, their robustness and linguistic adaptability position them as viable candidates for on-premises healthcare digitization.

Compact Multimodal Language Models as Robust OCR Alternatives for Noisy Textual Clinical Reports

State Space Models (SSMs) have recently emerged as efficient alternatives to Transformers for sequence modeling, yet extending them to two-dimensional vision tasks remains challenging. The Graph-Generating State Space Model (GG-SSM) addresses this challenge by constructing an adaptive graph, achieving competitive performance on vision benchmarks. However, state propagation over the resulting graph introduces substantial inference overhead, limiting scalability to high-resolution inputs. In this work, we introduce a leaf-guided computation pruning strategy that accelerates GG-SSM inference without modifying the underlying graph topology. Rather than removing nodes or edges, our approach selectively scales or bypasses secondary refinement computations associated with high-dissimilarity leaf nodes, while preserving the low-weight MST backbone. Experiments on multiple long-term time series forecasting benchmarks demonstrate consistent throughput improvements with controlled accuracy degradation across a range of pruning ratios. These results indicate that structure-aware computation pruning is an effective mechanism for improving the scalability of graph-based state space models.

Token Pruning for Improving Graph-Generating State Space Model Performance

Linear recurrent neural networks (LRNNs) provide a structured approach to sequence modeling that bridges classical linear dynamical systems and modern deep learning, offering both expressive power and theoretical guarantees on stability and trainability. In recent years, multiple LRNN-based architectures have been proposed, each introducing distinct parameterizations, discretization schemes, and implementation constraints. However, existing implementations are fragmented across different software frameworks, often rely on framework-specific optimizations, and in some cases require custom CUDA kernels or lack publicly available code altogether. As a result, using, comparing, or extending LRNNs requires substantial implementation effort. To address this, we introduce $\texttt{lrnnx}$, a unified software library that implements several modern LRNN architectures under a common interface. The library exposes multiple levels of control, allowing users to work directly with core components or higher-level model abstractions. $\texttt{lrnnx}$ aims to improve accessibility, reproducibility, and extensibility of LRNN research and applications. We make our code available under a permissive MIT license.

lrnnx: A library for Linear RNNs

This work evaluates the non-English and unstructured text compression performance of Large Language Models (LLMs) by comparing them with traditional baselines on datasets from eight most widely spoken languages. Experimental results show that the evaluated LLM (LLaMA-3.2-1B) was considerably outperformed by the baselines, particularly on non-English datasets, where its performance relative to the best baseline was more than three times worse than on English datasets on average. It also compressed unstructured English data up to more than twofold less effectively than plain English data. Traditional methods, however, remained largely dataset-agnostic. Surprisingly, the LLM achieved worse compression ratios on some datasets than others despite modeling them more accurately. Overall, the outcomes and substantially higher compression time and resource consumption indicate that current LLMs are highly impractical for the compression task, where traditional methods continue to excel. Codes are available at: https://github.com/mehranhaddadi13/llm_compress.

Comparing Text Compression Capabilities of Large Language Models with Traditional Compression Algorithms

Human cognition is deeply intertwined with a sense of time, known as \textit{Chronoception}. This sense allows us to judge how long facts remain valid and when knowledge becomes outdated. Despite progress in vision, language, and motor control, AI still struggles to reason about temporal validity. We introduce Chronocept, the first benchmark to model temporal validity as a continuous probability distribution over time. Using skew-normal curves fitted along semantically decomposed temporal axes, Chronocept captures nuanced patterns of emergence, decay, and peak relevance. It includes two datasets: Benchmark I (atomic facts) and Benchmark II (multi-sentence passages). Annotations show strong inter-annotator agreement (84\% and 89\%). Our baselines predict curve parameters - location, scale, and skewness - enabling interpretable, generalizable learning and outperforming classification-based approaches. Chronocept fills a foundational gap in AI's temporal reasoning, supporting applications in knowledge grounding, fact-checking, retrieval-augmented generation (RAG), and proactive agents. Code and data are publicly available.

Chronocept: Instilling a Sense of Time in Machines

Temporal information is an essential part of communication, and understanding language requires processing it effectively. 
Despite recent advances, Large Language Models (LLMs) still struggle with temporal understanding.
Existing benchmarks primarily focus on English and underexplore how linguistic structure contributes to temporal meaning.
As a result, temporal understanding in languages other than English remains largely understudied.
In this paper, we introduce TimeRes, a Turkish benchmark for evaluating temporal understanding of LLMs. 
TimeRes aims to investigate comprehension of Reichenbach's temporal points and reported speech through date arithmetic.
Our dataset includes 4,600 questions across 4 tasks at two levels of complexity, and presents a paired question formulation to distinguish temporal discourse understanding from temporal arithmetic capabilities.
We evaluated six LLMs, and demonstrated that models struggle to resolve reported speech and fail to generalize across word order variations.

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